Estimated crash avoidance with the hypothetical introduction of automated vehicles: a simulation based on experts’ assessment from French in-depth data

Objective We aimed to quantify, through simulations using real crash data, the number of potentially avoided crashes following different replacement levels of light vehicles by level-5 automated light vehicles (AVs). Methods Since level-5 AVs are not on the road yet, or are too rare, we simulated their introduction into traffic using a national database of all fatal crashes and 5% of injury crashes observed in France in 2011. We fictitiously replaced a certain proportion of light vehicles (LVs) involved in crashes by level-5 AVs, and applied crash avoidance probabilities estimated by a number of experts regarding the capabilities of AVs depending on specific configurations. Estimates of the percentage of avoided crashes per user configuration and according to three selected (10%, 50%, 100%) replacement levels were made, as well as estimates taking into account the relative weight of these crash configurations, and considering fatal and injury crashes separately. Results Our simulation suggests that a reduction of almost half of fatal crashes (56%) and injury crashes (46%) could be expected by replacing all LVs on the road with level-5 AVs. The introduction of AVs would be the least effective for crashes involving a vulnerable road user, especially motorcyclists. Conclusion This result represents encouraging prospects for the introduction of automated vehicles into traffic, while making it clear that, even with all light vehicles replaced with level 5-AVs, all issues would not be solved, especially for crashes involving motorcyclists, cyclists and pedestrians.

The impact of snowfall on crashes, traffic volume, and revenue on the New York State Thruway

Snow has numerous effects on traffic, including reduced traffic volumes, greater crash risk, and increased travel times. This research examines how snow affects crash risk, traffic volume, and toll revenue on the New York State Thruway. Daily data from January for a ten-year period (2010-2019) were analyzed for the Thruway from the Pennsylvania state line in western New York to Syracuse.Anywhere from 35-50 percent of crashes are associated with inclement weather, with smaller impacts, proportionally, in areas with greater traffic volumes. As expected, snow was almost always involved when weather was a factor. “Unsafe speed” was the most common cause of crashes in inclement weather with all other factors (e.g., animals, drowsiness) much less likely to play a role. The percentage of crashes resulting in an injury did not change significantly with inclement conditions when compared to crashes occurring in fair conditions, and there were too few fatal crashes to make any inferences about them.Daily snowfall rates predicted about 30 percent of the variation in crash numbers, with every 5.1 cm of snowfall resulting in an additional crash, except in Buffalo where 5.1 cm of snow resulted in an additional 2.6 crashes. Confirming earlier results, daily snowfall had a large impact on passenger vehicle counts while commercial vehicle counts were less affected. Revenue data showed a similar pattern, with passenger revenue typically decreasing by 3-5 percent per 2.5 cm of snow, while commercial revenue decreases were 1-4 percent per 2.5 cm of snow.

Multiple Correspondence Analysis of Wrong-Way Driving Fatal Crashes on Freeways

The objective of this study is to identify clusters of contributing factors associated with the occurrence of wrong-way driving (WWD) fatal crashes on freeways using the multiple correspondence analysis (MCA) method based on the Burt matrix with an adjustment of inertias. A total of 14 years (2004–2017) of WWD fatal crash data were extracted from the National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) database. A standard procedure was developed to extract the WWD crash information (including a total of 3,817 crashes) on freeways from the FARS. Each crash contains various characteristics of crashes, vehicles, and drivers, for example, crash time, crash location, vehicle type, driver age, and so forth. The MCA analysis used a total of 19 key variables with 67 defined categories. The results of this study indicate that four clusters of factors which, when combined, might contribute to the occurrence of some WWD fatal crashes. These four clusters were: (1) younger drivers, driving under the influence (DUI), midnight/early morning, lower speed limit (45–50 mph), urban areas, and street lighting; (2) older drivers, non-DUI drivers, and daylight; (3) dark/no light, 18:00 to 23:59 p.m., higher speed limits (65 mph or more), and rural areas; and (4) rain/snow/sleet/hail/fog, and wet road surface.

Verification of usRAP Risk Assessments for Run-Off and Head-On Crashes Using Field Data

The United States Road Assessment Program (usRAP) provides a systemic approach to estimate the risk of severe injury and fatal crashes along roadway segments based on the expected safety performance of roadway and roadside characteristics, together with a general estimation of traffic volume. Detailed crash data are not needed for safety assessments, providing advantages over more traditional crash-driven approaches. However, experiences with usRAP are limited to the United States and to date, the program has a growing but limited number of participating states. Verification of the adequacy of usRAP assessments is therefore of significant value, not only to identify strengths and limitations of the methodology within the U.S. context, but also to potentially expand the set of tools available to agencies. This paper presents a verification of usRAP risk assessments for run-off-road and head-on crashes using over 7,000 mi of coded segments and five years of crash data collected in Utah. Comparisons between risk estimations from usRAP and actual crash rates provided insights into the expected and observed effects of roadside objects and their distances from the lanes traveled, type of median present, and horizontal curves. A spatial correlation test also confirmed the agreement between usRAP risk assessments and crash data, providing additional promising indications of the suitability of this systemic methodology for safety applications.

Analysis of Fatal Truck-Involved Work Zone Crashes in Florida: Application of Tree-Based Models

This paper presents the results of an analysis focusing on large truck-involved work zone fatal crashes using seven-year crash data in the State of Florida. Decision tree/random forest models were applied to specifically detect critical crash patterns that result in a fatality outcome. Because of the imbalanced nature of crash severity data (very low frequency of fatal crashes compared with property damage only or injury), data were treated using random and systematic over-sampling techniques. Marginal effects were addressed using Shapley values to increase model explainability. From a methodological perspective, results showed that the combination of over-sampling techniques with ensemble random forests could significantly improve model performance in predicting fatal crashes (compared with conventional logistic regression models). Primary contributors included pedestrian involvement, lighting conditions, safety equipment, driver condition, driver age, and work zone locations. For pedestrian crashes, factors such as dark-not lighted conditions, distracted truck driver, and driver’s age (young drivers outside city limits, senior drivers inside city limits) were highly likely to be fatal. For non-pedestrian crashes, the combination of front airbag deployment with any restraint system other than shoulder and belt was quite likely to be fatal. Also, abnormal driver conditions increased the risk of a fatal outcome. Additionally, the presence of female drivers (as the second driver in multiple vehicle crashes) highly decreased crash severity, probably because females typically drive more carefully than males. Interestingly, truck driver actions and maneuvers as well as roadway design and other physical environment features (i.e., number of lanes, median type, roadway grade, and alignment) did not show significant contribution to the model.

Implementation of Road Safety Countermeasures in Four Cities to Reduce the Harmful Effects of Alcohol: A Progress Report

BackgroundBeginning in 2016, the Anheuser-Busch InBev Foundation (ABIF) provided funding to six pilot cities to implement evidence-based interventions to reduce the harmful use of alcohol and its deleterious consequences such as alcohol-impaired driving. The cities receiving funding are Alexandra Township in Johannesburg, South Africa; Brasilia, Brazil; Columbus, Ohio, United States; Jiangshan, China; Leuven, Belgium; and Zacatecas, Mexico.MethodsFour of the city pilot coalitions are implementing a wide array of interventions to deter driving under the influence (DUI). Columbus made efforts to get more judges to apply Ohio’s alcohol ignition interlock law and implemented and evaluated a Safe Rides program. Brasilia increased the number of roadside checkpoints and planned an educational campaign about the dangers of impaired driving to be delivered at bars by firefighters and paramedics. Alexandra expanded and upgraded the Metropolitan Police Department’s Alcohol Evidence Center (AEC). In Zacatecas, among other interventions, a new Driving While Intoxicated (DWI) facility is being constructed to expedite case processing and adjudication.ResultsIn Columbus, the evaluation of the Safe Rides program showed an estimated reduction of 2.9 impaired driving crashes but also an average increase of 0.4 alcoholic drink per program participant. There was a reduction in harmful alcohol use of .02% in 2017 associated with the Safe Rides campaign, but with no carryover to 2018. In Brasilia, the combined effect of the road safety measures and other factors resulted in a 35% decrease in traffic deaths between 2016 and 2019. In Alexandra, there were 46 fatal crashes over the Easter weekend in 2018, 25 fatal crashes in 2019 (46% reduction), and 3 fatal crashes in 2020 (88% reduction from 2019). To date, Zacatecas’ road safety measures have not yet been evaluated.ConclusionsFull implementation of the city pilots’ planned road safety interventions has been slow, and presently the COVID-19 pandemic has halted most operations. Interim evaluations can be conducted once a pilot city’s countermeasures are fully implemented and have operated for at least one year. ABIF should continue to donate funds to increase or enhance evidence-based DUI enforcement strategies while also implementing awareness campaigns to inform the public and enhance the deterrent effect of those efforts.

Modeling the System of Beliefs That Affect Driving Under the Influence of Cannabis and Alcohol in Washington State

Washington state has observed increases in polydrug use in fatal crashes, primarily involving the combination of cannabis and alcohol. The purpose of this article is to explore the belief system associated with driving under the influence of cannabis and alcohol (DUICA) in Washington state using structural equation modeling (SEM). A convenience sample ( n = 737) of surveys collected from adults in Washington state was analyzed using SEM to reveal the latent structure of the belief system associated with DUICA. The results of this analysis indicated that the reported DUICA behavior (frequency) was predicted by intention and willingness. Willingness also predicted intention. Intention and willingness were predicted by positive attitudes toward DUICA, as well as normative perceptions that it was acceptable to important people and common behavior for most people. These components were themselves predicted by corresponding beliefs (behavioral beliefs, normative beliefs, and control beliefs). Finally, these beliefs were also influenced by the values that were most important to the respondents. Based on these results, it is reasonable to speculate that strategies to change these beliefs may also reduce DUICA behavior and associated fatal crashes.

Impact of Florida’s prescription drug monitoring program on drug- related fatal vehicle crashes: a difference-in-differences approach

BackgroundPrescription drug use has soared in the USA within the last two decades. Prescription drugs can impair motor skills essential for the safe operation of a motor vehicle, and therefore can affect traffic safety. As one of the epicentres of the opioid epidemic, Florida has been struck by high opioid misuse and overdose rates, and has concurrently suffered major threats to traffic disruptions safety caused by driving under the influence of drugs. To prevent prescription opioid misuse in Florida, Prescription Drug Monitoring Programs (PDMPs) were implemented in September 2011.ObjectiveTo examine the impact of Florida’s implementation of a mandatory PDMP on drug-related MVCs occurring on public roads.MethodsWe employed a difference-in-differences approach to estimate the difference in prescription drug-related fatal crashes in Florida associated with its 2011 PDMP implementation relative to those in Georgia, which did not use PDMPs during the same period (2009–2013). The analyses were conducted in 2020.ResultsIn Florida, there was a significant decline in drug-related vehicle crashes during the 22 months post-PDMP. PDMP implementation was associated with approximately two (−2.21; 95% CI −4.04 to –0.37; p<0.05) fewer prescribed opioid-related fatal crashes every month, indicating 25% reduction in the number of monthly crashes. We conducted sensitivity analyses to investigate the impact of PDMP implementation on central nervous system depressants and stimulants as well as cocaine and marijuana-related fatal crashes but found no robust significant reductions.ConclusionsThe implementation of PDMPs in Florida provided important benefits for traffic safety, reducing the rates of prescription opioid-related vehicle crashes.